Abstract
Objective
One approach to overcoming potential biased results, due to dropout from longitudinal
clinical studies, is to capture additional data once a marker of health downturn is
observed but before the patient leaves the study. We denote this study design feature
as “triggered sampling” (TS).
Study Design and Setting
We formally define TS, describe some mechanisms for incorporating TS in longitudinal
studies, and present the results from a 2-year longitudinal observational study of
treatment preferences, measured on a 1–7 scale, of patients with advanced illness
from cancer, congestive heart failure, or chronic obstructive pulmonary disease. We
examined the utility of TS through multiple analyses, including mixed effects models.
Results
One hundred forty-eight of 226 participants experienced at least one triggered interview.
Those who did not drop out after their first trigger had no noticeable change in their
mean preferences (6.20 pretrigger, 6.16 trigger, P=0.76), whereas those who dropped out after their first trigger did (6.29 pretrigger,
5.69 trigger, P=0.04). The mixed effects models conveyed similar results, providing support for the
efficiency and efficacy of TS.
Conclusion
TS can help alleviate bias due to impending dropout and potentially be a valuable
addition to the designs of longitudinal studies of persons with elevated mortality
risk.
Keywords
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Article info
Publication history
Published online: September 29, 2006
Accepted:
June 2,
2006
Identification
Copyright
© 2007 Elsevier Inc. Published by Elsevier Inc. All rights reserved.
